一种用于实时个性化人体工学姿态分析的机器学习框架
A Machine Learning Framework for Real-Time Personalized Ergonomic Pose Analysis
AI总结：
提出利用三维体积视频数据实时预测人体工学/非工学姿态的方法，结合3D点云多角度分析与个性化深度学习分类器，克服固定视角遮挡问题，实现实时评估。
摘要：
本文介绍了一种利用三维体积视频数据实时预测人体工学和非工学姿态的新方法。尽管该方法是为人体工学评估设计的，但它可以适应其他需要实时分析人体姿态的应用。该系统的一个突出特点是能够在评估过程中分析3D点云，从而实现多角度计算。这克服了相机通常提供固定视角的关键限制，从而限制了全面姿态评估可用的数据，尤其是在发生遮挡时。系统持续自动地对实时流数据使用选定的视角进行姿态推断；然而，只有用户手动选择和标记的姿态用于训练个性化深度学习分类器。该方法通过一个案例研究进行了优化，其中RGB-D相机捕捉了执行负重任务的受试者，实现了实时骨骼标记。模型在此数据上训练，并在训练阶段后对新流数据实时进行推断。本研究通过结合最先进的3D数据技术和传统的2D姿态估计算法，为实时人体工学评估提供了一种可扩展且实用的方法。它解决了工作场所环境中日益增长的安全与健康监测需求，标志着对该领域的显著贡献。
英文摘要：
This paper introduces a new methodology for real-time prediction of ergonomic and non-ergonomic human poses using volumetric video data in three dimensions. Although the methodology was designed for ergonomic assessments, it can be adapted to other applications requiring real-time analysis of human posture. One aspect that makes this system stand out is its ability to analyze 3D point clouds during the assessment, enabling computation from multiple angles. This overcomes a critical limitation of cameras which provide often a fixed viewpoint, thereby restricting the data available for a thorough postural evaluation, especially when occlusions occur. The system continuously and automatically performs pose inference using the chosen perspective on the real-time streaming data; however, only the poses manually selected and labeled by the user are used to train the personalized deep learning classifier. The methodology has been refined through a case study in which RGB-D cameras captured subjects performing load-lifting tasks, enabling real-time skeletal labeling. The model was trained on this data and, following the training phase, performs inference on new streaming data in real time. This research offers a scalable and pragmatic approach for real-time ergonomic evaluation by combining state-of-the-art 3D data technologies and traditional 2D pose estimation algorithms. It addresses the increasing need for safety and health monitoring in workplace environments, marking a notable contribution to the domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
